Retention of provisionally licensed international medical graduates: a historical cohort study of general and family physicians in Newfoundland and Labrador.
Bibliographic record
Abstract
BACKGROUND: To alleviate the shortage of primary care physicians in rural communities, the Canadian province of Newfoundland and Labrador (NL) introduced provisional licensure for international medical graduates (IMGs), allowing them to practise in under-served communities while completing licensing requirements. Although provisional licensing has been seen as a needed recruitment strategy, little is known about its impact on physician retention. To assess the relationship between provincial retention time and type of initial practice licence, we compared the retention of: (1) IMGs who began practice with a provisional licence; (2) fully licensed Memorial University medical graduates (MMGs); and (3) fully licensed medical graduates from other Canadian medical schools (CMGs). METHODS: Using administrative data from the NL College of Physicians and Surgeons, the 2004 Scott's Medical Database, and the Memorial University postgraduate database, we identified family physicians/general practitioners (FPs/GPs) who began their practice in NL in the period 1997-2000 and determined where they were in 2004. We used Cox regression to examine differences in retention among these 3 groups of physicians. RESULTS: There were 42 MMGs, 38 CMGs and 77 IMGs in our sample. The median time for IMGs to qualify for full licensure was 15 months. Twenty-one physicians (13.4%) stayed in NL after beginning their practice (35.7% MMGs, 5.3% CMGs, 5.2% IMGs; p < 0.000). The median retention time was 25 months (MMGs, 39 months; CMGs, 22 months; IMGs, 22 months; p < 0.000). After controlling for Certificant of the College of Family Physicians status, CMGs (hazard ratio [HR] = 2.15; 95% confidence interval [CI] 1.29-3.60) and IMGs (HR = 2.03; 95% CI 1.26-3.27) were more likely to leave NL than MMGs. CONCLUSIONS: Provisional licensing accounts for the largest proportion of new primary care physicians in NL but does not lead to long-term retention of IMGs. However, IMG retention is no worse than the retention of CMGs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".